Diet, physical activity, glucose-insulin control and autonomic activity are tied together in a delicate balance that, if disrupted, can lead to obesity and obesity-related disorders. Sleep disordered breathing (SDB), which is highly prevalent in obesity, can also contribute independently to autonomic imbalance and insulin resistance. Recent studies also suggest that the vicious cycle of interplay among these factors in childhood predisposes to the emergence of "metabolic syndrome", a clustering of obesity, hypertension, insulin resistance and dyslipidemia. Based on extensive preliminary data, we hypothesize that the strong association between autonomic and metabolic function enables the use of autonomic markers as noninvasive surrogate measures of insulin sensitivity in obese children that could be applied in clinical and community settings. To test this hypothesis, we will: (1) develop a method for noninvasive assessment of autonomic function, based on a computational model of pulse transit time variability and heart rate variability;(2) determine the quantitative relationships between the parameters of the autonomic control model and insulin sensitivity in childhood obesity;and (3) determine how SDB alters these relationships. The study employs a multidisciplinary approach with expertise in computational bioengineering, cardiopulmonary physiology, pediatric sleep disorders and pediatric obesity research. We will initiate this study in a homogenous sample of obese male Hispanic children aged 13-17 years, with and without SDB. Autonomic measurements (respiration, heart rate, blood pressure and pulse transit time) will be monitored during supine wakefulness and following exposure to autonomic challenges (cold face test and orthostatic stress). From these measurements, the parameters of a computational model of heart rate and pulse transit variability will be estimated and used to quantify baseline autonomic function and cardiovascular autonomic reactivity. Metabolic measurements will include body composition, fasting levels of insulin, glucose and triglyceride, and a frequently sampled intravenous glucose tolerance test. Regression analysis will be used to determine the correlations between the parameters representing autonomic and metabolic function, as well as how these correlations are affected by SDB. The knowledge derived from this study may lead to the development of a low-cost, portable device that can be used for early detection/monitoring of autonomic and metabolic abnormalities and sleep disruption in large populations of obese children.